Overview

Dataset statistics

Number of variables11
Number of observations441
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.0 KiB
Average record size in memory88.3 B

Variable types

Text2
Categorical2
Numeric7

Alerts

Camere tot is highly overall correlated with Numero stelle and 2 other fieldsHigh correlation
Codice via is highly overall correlated with MunicipioHigh correlation
Municipio is highly overall correlated with Codice viaHigh correlation
Numero stelle is highly overall correlated with Camere tot and 2 other fieldsHigh correlation
Piani totali is highly overall correlated with Camere tot and 2 other fieldsHigh correlation
Posti letto tot is highly overall correlated with Camere tot and 2 other fieldsHigh correlation
Tipo via is highly imbalanced (58.8%)Imbalance
Codice via has 7 (1.6%) zerosZeros
Municipio has 7 (1.6%) zerosZeros

Reproduction

Analysis started2026-01-21 12:58:29.331792
Analysis finished2026-01-21 12:58:31.242009
Duration1.91 second
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Nome
Text

Distinct431
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2026-01-21T13:58:31.353498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length142
Median length33
Mean length15.095238
Min length3

Characters and Unicode

Total characters6657
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique428 ?
Unique (%)97.1%

Sample

1st rowACCA PALACE
2nd rowALBERGO ACCURSIO
3rd rowALBERGO DEL SOLE
4th rowALBERGO FELICE CASATI
5th rowALBERGO FENICE
ValueCountFrequency (%)
hotel272
25.6%
residence32
 
3.0%
milano26
 
2.5%
albergo17
 
1.6%
unknown9
 
0.8%
milan9
 
0.8%
la8
 
0.8%
di8
 
0.8%
pensione7
 
0.7%
san7
 
0.7%
Other values (530)666
62.8%
2026-01-21T13:58:31.500886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E780
11.7%
O700
10.5%
630
9.5%
L580
8.7%
A573
8.6%
T500
 
7.5%
I426
 
6.4%
N400
 
6.0%
R353
 
5.3%
H325
 
4.9%
Other values (33)1390
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)6657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E780
11.7%
O700
10.5%
630
9.5%
L580
8.7%
A573
8.6%
T500
 
7.5%
I426
 
6.4%
N400
 
6.0%
R353
 
5.3%
H325
 
4.9%
Other values (33)1390
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E780
11.7%
O700
10.5%
630
9.5%
L580
8.7%
A573
8.6%
T500
 
7.5%
I426
 
6.4%
N400
 
6.0%
R353
 
5.3%
H325
 
4.9%
Other values (33)1390
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E780
11.7%
O700
10.5%
630
9.5%
L580
8.7%
A573
8.6%
T500
 
7.5%
I426
 
6.4%
N400
 
6.0%
R353
 
5.3%
H325
 
4.9%
Other values (33)1390
20.9%

Tipologia
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
ALBERGO
391 
RESIDENCE
50 

Length

Max length9
Median length7
Mean length7.2267574
Min length7

Characters and Unicode

Total characters3187
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRESIDENCE
2nd rowALBERGO
3rd rowALBERGO
4th rowALBERGO
5th rowALBERGO

Common Values

ValueCountFrequency (%)
ALBERGO391
88.7%
RESIDENCE50
 
11.3%

Length

2026-01-21T13:58:31.538226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-21T13:58:31.565508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
albergo391
88.7%
residence50
 
11.3%

Most occurring characters

ValueCountFrequency (%)
E541
17.0%
R441
13.8%
A391
12.3%
L391
12.3%
B391
12.3%
G391
12.3%
O391
12.3%
S50
 
1.6%
I50
 
1.6%
D50
 
1.6%
Other values (2)100
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E541
17.0%
R441
13.8%
A391
12.3%
L391
12.3%
B391
12.3%
G391
12.3%
O391
12.3%
S50
 
1.6%
I50
 
1.6%
D50
 
1.6%
Other values (2)100
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E541
17.0%
R441
13.8%
A391
12.3%
L391
12.3%
B391
12.3%
G391
12.3%
O391
12.3%
S50
 
1.6%
I50
 
1.6%
D50
 
1.6%
Other values (2)100
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E541
17.0%
R441
13.8%
A391
12.3%
L391
12.3%
B391
12.3%
G391
12.3%
O391
12.3%
S50
 
1.6%
I50
 
1.6%
D50
 
1.6%
Other values (2)100
 
3.1%

Numero stelle
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7505669
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)2.7%
Memory size3.6 KiB
2026-01-21T13:58:31.586556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum6
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.297342
Coefficient of variation (CV)0.4716635
Kurtosis0.14185225
Mean2.7505669
Median Absolute Deviation (MAD)1
Skewness-0.70157885
Sum1213
Variance1.6830963
MonotonicityNot monotonic
2026-01-21T13:58:31.614029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4146
33.1%
3130
29.5%
181
18.4%
264
14.5%
-112
 
2.7%
56
 
1.4%
62
 
0.5%
ValueCountFrequency (%)
-112
 
2.7%
181
18.4%
264
14.5%
3130
29.5%
4146
33.1%
56
 
1.4%
62
 
0.5%
ValueCountFrequency (%)
62
 
0.5%
56
 
1.4%
4146
33.1%
3130
29.5%
264
14.5%
181
18.4%
-112
 
2.7%

Tipo via
Categorical

Imbalance 

Distinct9
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
VIA
333 
VLE
47 
CSO
 
27
PZA
 
23
PLE
 
4
Other values (4)
 
7

Length

Max length7
Median length3
Mean length3.0090703
Min length3

Characters and Unicode

Total characters1327
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st rowVIA
2nd rowVLE
3rd rowVIA
4th rowVIA
5th rowCSO

Common Values

ValueCountFrequency (%)
VIA333
75.5%
VLE47
 
10.7%
CSO27
 
6.1%
PZA23
 
5.2%
PLE4
 
0.9%
LGO3
 
0.7%
GLL2
 
0.5%
ALZ1
 
0.2%
UNKNOWN1
 
0.2%

Length

2026-01-21T13:58:31.645583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-21T13:58:31.673975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
via333
75.5%
vle47
 
10.7%
cso27
 
6.1%
pza23
 
5.2%
ple4
 
0.9%
lgo3
 
0.7%
gll2
 
0.5%
alz1
 
0.2%
unknown1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
V380
28.6%
A357
26.9%
I333
25.1%
L59
 
4.4%
E51
 
3.8%
O31
 
2.3%
C27
 
2.0%
S27
 
2.0%
P27
 
2.0%
Z24
 
1.8%
Other values (5)11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V380
28.6%
A357
26.9%
I333
25.1%
L59
 
4.4%
E51
 
3.8%
O31
 
2.3%
C27
 
2.0%
S27
 
2.0%
P27
 
2.0%
Z24
 
1.8%
Other values (5)11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V380
28.6%
A357
26.9%
I333
25.1%
L59
 
4.4%
E51
 
3.8%
O31
 
2.3%
C27
 
2.0%
S27
 
2.0%
P27
 
2.0%
Z24
 
1.8%
Other values (5)11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V380
28.6%
A357
26.9%
I333
25.1%
L59
 
4.4%
E51
 
3.8%
O31
 
2.3%
C27
 
2.0%
S27
 
2.0%
P27
 
2.0%
Z24
 
1.8%
Other values (5)11
 
0.8%
Distinct307
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2026-01-21T13:58:31.752052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length22
Mean length13.113379
Min length4

Characters and Unicode

Total characters5783
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)51.9%

Sample

1st rowNICOTERA GIOVANNI
2nd rowCERTOSA
3rd rowSPONTINI GASPARE
4th rowCASATI FELICE
5th rowBUENOS AIRES
ValueCountFrequency (%)
giovanni19
 
2.4%
giuseppe14
 
1.8%
antonio14
 
1.8%
carlo13
 
1.6%
nicola10
 
1.3%
torriani9
 
1.1%
della9
 
1.1%
napo9
 
1.1%
dei8
 
1.0%
battista8
 
1.0%
Other values (430)678
85.7%
2026-01-21T13:58:31.866410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A662
11.4%
O650
11.2%
I641
11.1%
E424
 
7.3%
N391
 
6.8%
R383
 
6.6%
L359
 
6.2%
350
 
6.1%
T273
 
4.7%
C239
 
4.1%
Other values (18)1411
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)5783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A662
11.4%
O650
11.2%
I641
11.1%
E424
 
7.3%
N391
 
6.8%
R383
 
6.6%
L359
 
6.2%
350
 
6.1%
T273
 
4.7%
C239
 
4.1%
Other values (18)1411
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A662
11.4%
O650
11.2%
I641
11.1%
E424
 
7.3%
N391
 
6.8%
R383
 
6.6%
L359
 
6.2%
350
 
6.1%
T273
 
4.7%
C239
 
4.1%
Other values (18)1411
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A662
11.4%
O650
11.2%
I641
11.1%
E424
 
7.3%
N391
 
6.8%
R383
 
6.6%
L359
 
6.2%
350
 
6.1%
T273
 
4.7%
C239
 
4.1%
Other values (18)1411
24.4%

Civico
Real number (ℝ)

Distinct92
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.566893
Minimum1
Maximum371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2026-01-21T13:58:31.904045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median15
Q333
95-th percentile87
Maximum371
Range370
Interquartile range (IQR)27

Descriptive statistics

Standard deviation37.505887
Coefficient of variation (CV)1.3605409
Kurtosis26.637453
Mean27.566893
Median Absolute Deviation (MAD)10
Skewness4.140182
Sum12157
Variance1406.6915
MonotonicityNot monotonic
2026-01-21T13:58:31.943774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
623
 
5.2%
223
 
5.2%
421
 
4.8%
1217
 
3.9%
117
 
3.9%
1016
 
3.6%
315
 
3.4%
1815
 
3.4%
814
 
3.2%
1414
 
3.2%
Other values (82)266
60.3%
ValueCountFrequency (%)
117
3.9%
223
5.2%
315
3.4%
421
4.8%
513
2.9%
623
5.2%
712
2.7%
814
3.2%
912
2.7%
1016
3.6%
ValueCountFrequency (%)
3711
0.2%
3001
0.2%
2781
0.2%
1701
0.2%
1531
0.2%
1431
0.2%
1391
0.2%
1341
0.2%
1322
0.5%
1251
0.2%

Codice via
Real number (ℝ)

High correlation  Zeros 

Distinct303
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3072.5011
Minimum0
Maximum7505
Zeros7
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2026-01-21T13:58:31.983792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300
Q11292
median2246
Q35097
95-th percentile7188
Maximum7505
Range7505
Interquartile range (IQR)3805

Descriptive statistics

Standard deviation2184.671
Coefficient of variation (CV)0.71103993
Kurtosis-0.78348296
Mean3072.5011
Median Absolute Deviation (MAD)1107
Skewness0.66149147
Sum1354973
Variance4772787.3
MonotonicityNot monotonic
2026-01-21T13:58:32.089132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21269
 
2.0%
22298
 
1.8%
21347
 
1.6%
07
 
1.6%
21295
 
1.1%
31155
 
1.1%
71744
 
0.9%
21124
 
0.9%
31834
 
0.9%
71884
 
0.9%
Other values (293)384
87.1%
ValueCountFrequency (%)
07
1.6%
11
 
0.2%
1051
 
0.2%
1151
 
0.2%
1231
 
0.2%
1391
 
0.2%
1441
 
0.2%
1461
 
0.2%
1901
 
0.2%
2072
 
0.5%
ValueCountFrequency (%)
75051
0.2%
75001
0.2%
74251
0.2%
74201
0.2%
73961
0.2%
73901
0.2%
73821
0.2%
73601
0.2%
72761
0.2%
72721
0.2%

Municipio
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0612245
Minimum0
Maximum9
Zeros7
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2026-01-21T13:58:32.120202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.6668619
Coefficient of variation (CV)0.65666448
Kurtosis-1.0189618
Mean4.0612245
Median Absolute Deviation (MAD)2
Skewness0.56599633
Sum1791
Variance7.1121521
MonotonicityNot monotonic
2026-01-21T13:58:32.144809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3106
24.0%
171
16.1%
271
16.1%
845
10.2%
935
 
7.9%
733
 
7.5%
429
 
6.6%
526
 
5.9%
618
 
4.1%
07
 
1.6%
ValueCountFrequency (%)
07
 
1.6%
171
16.1%
271
16.1%
3106
24.0%
429
 
6.6%
526
 
5.9%
618
 
4.1%
733
 
7.5%
845
10.2%
935
 
7.9%
ValueCountFrequency (%)
935
 
7.9%
845
10.2%
733
 
7.5%
618
 
4.1%
526
 
5.9%
429
 
6.6%
3106
24.0%
271
16.1%
171
16.1%
07
 
1.6%

Camere tot
Real number (ℝ)

High correlation 

Distinct147
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.936508
Minimum7
Maximum439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2026-01-21T13:58:32.176457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10
Q116
median32
Q373
95-th percentile228
Maximum439
Range432
Interquartile range (IQR)57

Descriptive statistics

Standard deviation70.082001
Coefficient of variation (CV)1.1692707
Kurtosis7.3627571
Mean59.936508
Median Absolute Deviation (MAD)21
Skewness2.5086017
Sum26432
Variance4911.4869
MonotonicityNot monotonic
2026-01-21T13:58:32.215811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1024
 
5.4%
1115
 
3.4%
1414
 
3.2%
1514
 
3.2%
1311
 
2.5%
1811
 
2.5%
1611
 
2.5%
1710
 
2.3%
1210
 
2.3%
710
 
2.3%
Other values (137)311
70.5%
ValueCountFrequency (%)
710
2.3%
86
 
1.4%
96
 
1.4%
1024
5.4%
1115
3.4%
1210
2.3%
1311
2.5%
1414
3.2%
1514
3.2%
1611
2.5%
ValueCountFrequency (%)
4391
0.2%
4231
0.2%
4201
0.2%
3281
0.2%
3271
0.2%
3231
0.2%
3201
0.2%
3131
0.2%
3051
0.2%
2981
0.2%

Piani totali
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6462585
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2026-01-21T13:58:32.243870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile9
Maximum17
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1964932
Coefficient of variation (CV)0.47274452
Kurtosis4.3535184
Mean4.6462585
Median Absolute Deviation (MAD)1
Skewness1.4073375
Sum2049
Variance4.8245826
MonotonicityNot monotonic
2026-01-21T13:58:32.271615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3113
25.6%
494
21.3%
572
16.3%
646
10.4%
734
 
7.7%
221
 
4.8%
118
 
4.1%
818
 
4.1%
914
 
3.2%
106
 
1.4%
Other values (4)5
 
1.1%
ValueCountFrequency (%)
118
 
4.1%
221
 
4.8%
3113
25.6%
494
21.3%
572
16.3%
646
10.4%
734
 
7.7%
818
 
4.1%
914
 
3.2%
106
 
1.4%
ValueCountFrequency (%)
172
 
0.5%
131
 
0.2%
121
 
0.2%
111
 
0.2%
106
 
1.4%
914
 
3.2%
818
 
4.1%
734
7.7%
646
10.4%
572
16.3%

Posti letto tot
Real number (ℝ)

High correlation 

Distinct194
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.77551
Minimum7
Maximum922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2026-01-21T13:58:32.306667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile17
Q125
median60
Q3145
95-th percentile439
Maximum922
Range915
Interquartile range (IQR)120

Descriptive statistics

Standard deviation139.15683
Coefficient of variation (CV)1.2230825
Kurtosis8.0759024
Mean113.77551
Median Absolute Deviation (MAD)38
Skewness2.5882181
Sum50175
Variance19364.624
MonotonicityNot monotonic
2026-01-21T13:58:32.344334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2535
 
7.9%
2015
 
3.4%
2414
 
3.2%
2311
 
2.5%
329
 
2.0%
219
 
2.0%
168
 
1.8%
487
 
1.6%
997
 
1.6%
227
 
1.6%
Other values (184)319
72.3%
ValueCountFrequency (%)
71
 
0.2%
91
 
0.2%
101
 
0.2%
111
 
0.2%
124
0.9%
142
 
0.5%
153
 
0.7%
168
1.8%
176
1.4%
186
1.4%
ValueCountFrequency (%)
9221
0.2%
8641
0.2%
7921
0.2%
7361
0.2%
6501
0.2%
6461
0.2%
6361
0.2%
6231
0.2%
5771
0.2%
5381
0.2%

Interactions

2026-01-21T13:58:30.948934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.547127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.771986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.989646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.210806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.408641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.726391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.979087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.584101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.804675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.022751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.239885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.437592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.764941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:31.008822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.617103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.835581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.054115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.268300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.550512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.797331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:31.039606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.649700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.868315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.087075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.298790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.579802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.829660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:31.065887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.680705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.898423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.116753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.324382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.606403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.859384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:31.094684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.709943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.927191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.147605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.351965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.632818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.888470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:31.124300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.742598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:29.959495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.179196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.380507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.661700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T13:58:30.919859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-21T13:58:32.373871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Camere totCivicoCodice viaMunicipioNumero stellePiani totaliPosti letto totTipo viaTipologia
Camere tot1.000-0.080-0.162-0.0770.7070.8170.9820.0980.040
Civico-0.0801.0000.1290.094-0.142-0.082-0.0860.0000.000
Codice via-0.1620.1291.0000.585-0.131-0.160-0.1560.0780.000
Municipio-0.0770.0940.5851.000-0.060-0.128-0.0800.1630.164
Numero stelle0.707-0.142-0.131-0.0601.0000.7110.7020.0000.162
Piani totali0.817-0.082-0.160-0.1280.7111.0000.8030.0000.104
Posti letto tot0.982-0.086-0.156-0.0800.7020.8031.0000.1060.066
Tipo via0.0980.0000.0780.1630.0000.0000.1061.0000.000
Tipologia0.0400.0000.0000.1640.1620.1040.0660.0001.000

Missing values

2026-01-21T13:58:31.170212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-21T13:58:31.208016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NomeTipologiaNumero stelleTipo viaNome viaCivicoCodice viaMunicipioCamere totPiani totaliPosti letto tot
0ACCA PALACERESIDENCE4VIANICOTERA GIOVANNI91508941582
1ALBERGO ACCURSIOALBERGO3VLECERTOSA887174827439
2ALBERGO DEL SOLEALBERGO1VIASPONTINI GASPARE62141317240
3ALBERGO FELICE CASATIALBERGO4VIACASATI FELICE1821223993145
4ALBERGO FENICEALBERGO3CSOBUENOS AIRES22129346498
5ALBERGO IRIDEALBERGO2VIAPORPORA NICOLA ANTONIO1702229311319
6ALBERGO LA PACEALBERGO1VIACATALANI ALFREDO692425320633
7ALBERGO LARIOALBERGO1VIALARIO40117197317
8ALBERGO LOMBARDIAALBERGO3VLELOMBARDIA7424003965157
9ALBERGO MARTEALBERGO2VIASFORZA ASCANIO815201515325
NomeTipologiaNumero stelleTipo viaNome viaCivicoCodice viaMunicipioCamere totPiani totaliPosti letto tot
431ZEFIROALBERGO4VIAGALLINA GIACINTO123105354592
432UNKNOWNALBERGO5CSOCONCORDIA131163776180
433UNKNOWNALBERGO3PZASANT' EUSTORGIO25186122438
434UNKNOWNRESIDENCE2VIABREMBO274219555499
435UNKNOWNALBERGO4VIAFELTRE192652338570
436UNKNOWNRESIDENCE2VIAIPPODROMO8649281165232
437UNKNOWNALBERGO4VIAORSEOLO PIETRO151136595101
438UNKNOWNALBERGO4VIASAN TOMASO8723111422
439UNKNOWNALBERGO4VIASTEPHENSON GIORGIO55002569512
440UNKNOWNALBERGO4VIAVENEZIA GIULIA9750081156230